{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 残差网络（ResNet）\n",
    "让我们先思考一个问题：对神经网络模型添加新的层，充分训练后的模型是否只可能更有效地降低训练误差？理论上，原模型解的空间只是新模型解的空间的子空间。也就是说，如果我们能将新添加的层训练成恒等映射f(x) = x，新模型和原模型将同样有效。由于新模型可能得出更优的解来拟合训练数据集，因此添加层似乎更容易降低训练误差。然而在实践中，添加过多的层后训练误差往往不降反升。即使利用批量归一化带来的数值稳定性使训练深层模型更加容易，该问题仍然存在。针对这一问题，何恺明等人提出了残差网络（ResNet）。它在2015年的ImageNet图像识别挑战赛夺魁，并深刻影响了后来的深度神经网络的设计。"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 残差块\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "import sys\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "class Residual(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, use_1x1conv=False, stride=1):\n",
    "        super(Residual, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride)\n",
    "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)\n",
    "        if use_1x1conv:\n",
    "            self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride)\n",
    "        else:\n",
    "            self.conv3 = None\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "    \n",
    "    def forward(self, X):\n",
    "        Y = F.relu(self.bn1(self.conv1(X)))\n",
    "        Y = self.bn2(self.conv2(Y))\n",
    "        if self.conv3:\n",
    "            X = self.conv3(X)\n",
    "        return F.relu(Y + X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 3, 6, 6])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 输入和输出形状一样的情况\n",
    "blk = Residual(in_channels=3, out_channels=3)\n",
    "x = torch.rand((4, 3, 6, 6))\n",
    "blk(x).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 6, 3, 3])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 也可以在增加输出通道数的同时减半输出的高和宽\n",
    "blk = Residual(3, 6, use_1x1conv=True, stride=2)\n",
    "blk(x).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ResNet模型\n",
    "ResNet的前两层跟之前介绍的GoogLeNet中的一样：在输出通道数为64、步幅为2的7×7卷积层后接步幅为2的3×3的最大池化层。不同之处在于ResNet每个卷积层后增加的批量归一化层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = nn.Sequential(\n",
    "    nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),\n",
    "    nn.BatchNorm2d(64),\n",
    "    nn.ReLU(),\n",
    "    nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GoogLeNet在后面接了4个由Inception块组成的模块。ResNet则使用4个由残差块组成的模块，每个模块使用若干个同样输出通道数的残差块。第一个模块的通道数同输入通道数一致。由于之前已经使用了步幅为2的最大池化层，所以无须减小高和宽。之后的每个模块在第一个残差块里将上一个模块的通道数翻倍，并将高和宽减半。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注意，这里对第一个模块做了特别处理\n",
    "\n",
    "def resnet_block(in_channels, out_channels, num_residuals, first_block=False):\n",
    "    if first_block:\n",
    "        assert in_channels == out_channels # 第一个模块的通道数同输入通道数一致\n",
    "    blk = []\n",
    "    for i in range(num_residuals):\n",
    "        if i==0 and not first_block:\n",
    "            blk.append(Residual(in_channels, out_channels, use_1x1conv=True, stride=2))\n",
    "        else:\n",
    "            blk.append(Residual(out_channels, out_channels))\n",
    "    return nn.Sequential(*blk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为ResNet加入所有残差块,这里每个模块使用两个残差块\n",
    "net.add_module(\"resnet_block1\", resnet_block(64, 64, 2, first_block=True))\n",
    "net.add_module(\"resnet_block2\", resnet_block(64, 128, 2))\n",
    "net.add_module(\"resnet_block3\", resnet_block(128, 256, 2))\n",
    "net.add_module(\"resnet_block4\", resnet_block(256, 512, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GlobalAvgPool2d(nn.Module):\n",
    "    # 全局平均池化层可通过将池化窗口形状设置成输入的高和宽实现\n",
    "    def __init__(self):\n",
    "        super(GlobalAvgPool2d, self).__init__()\n",
    "    def forward(self, x):\n",
    "        return F.avg_pool2d(x, kernel_size=x.size()[2:])\n",
    "\n",
    "class FlattenLayer(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(FlattenLayer, self).__init__()\n",
    "    def forward(self, x):\n",
    "        return x.view(x.shape[0], -1)\n",
    "\n",
    "# 最后，与GoogLeNet一样，加入全局平均池化层后接上全连接层输出\n",
    "net.add_module(\"global_avg_pool\", GlobalAvgPool2d()) # GlobalAvgPool2d的输出: (Batch, 512, 1, 1)\n",
    "net.add_module(\"fc\", nn.Sequential(FlattenLayer(), nn.Linear(512, 10)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里每个模块里有4个卷积层（不计算1×1卷积层），加上最开始的卷积层和最后的全连接层，共计18层。这个模型通常也被称为ResNet-18。通过配置不同的通道数和模块里的残差块数可以得到不同的ResNet模型，例如更深的含152层的ResNet-152。虽然ResNet的主体架构跟GoogLeNet的类似，但ResNet结构更简单，修改也更方便。这些因素都导致了ResNet迅速被广泛使用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 output shape:\t torch.Size([1, 64, 48, 48])\n",
      "1 output shape:\t torch.Size([1, 64, 48, 48])\n",
      "2 output shape:\t torch.Size([1, 64, 48, 48])\n",
      "3 output shape:\t torch.Size([1, 64, 24, 24])\n",
      "resnet_block1 output shape:\t torch.Size([1, 64, 24, 24])\n",
      "resnet_block2 output shape:\t torch.Size([1, 128, 12, 12])\n",
      "resnet_block3 output shape:\t torch.Size([1, 256, 6, 6])\n",
      "resnet_block4 output shape:\t torch.Size([1, 512, 3, 3])\n",
      "global_avg_pool output shape:\t torch.Size([1, 512, 1, 1])\n",
      "fc output shape:\t torch.Size([1, 10])\n"
     ]
    }
   ],
   "source": [
    "# 观察一下输入形状在ResNet不同模块之间的变化\n",
    "x = torch.rand(1, 1, 96, 96)\n",
    "for name,layer in net.named_children():\n",
    "    x = layer(x)\n",
    "    print(name, 'output shape:\\t', x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取数据训练模型\n",
    "import sys\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "def load_data_fashion_mnist(batch_size, resize=None, root=\"Datasets/FashionMNIST\"):\n",
    "    \"\"\"Download the fashion mnist dataset and then load into memory.\"\"\"\n",
    "    trans = []\n",
    "    if resize:\n",
    "        trans.append(transforms.Resize(size=resize))\n",
    "    trans.append(transforms.ToTensor())\n",
    "    transform = transforms.Compose(trans)\n",
    "    mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform)\n",
    "    mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform)\n",
    "    \n",
    "    if sys.platform.startswith('win'):\n",
    "        num_workers = 4\n",
    "    else:\n",
    "        num_workers = 4\n",
    "    train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=4)\n",
    "    test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=4)\n",
    "    return train_iter, test_iter\n",
    "\n",
    "def evaluate_accuracy(data_iter, net, device=None):\n",
    "    if device is None and isinstance(net, torch.nn.Module):\n",
    "        # 如果没有指定device，就使用net的device\n",
    "        device = list(net.parameters())[0].device\n",
    "    acc_sum, n = 0.0, 0\n",
    "    with torch.no_grad():\n",
    "        for X, y in data_iter:\n",
    "            if isinstance(net, torch.nn.Module):\n",
    "                net.eval() # 评估模型，这会关闭dropout\n",
    "                acc_sum += (net(X.to(device)).argmax(dim=1)==y.to(device)).float().sum().cpu().item()\n",
    "                net.train() # 改回训练模型\n",
    "            else: # 自定义的模型，不考虑使用GPU\n",
    "                if ('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数\n",
    "                    # 将is_training设置成False\n",
    "                    acc_sum += (net(x, is_training=False).argmax(dim=1)==y).float().sum().item()\n",
    "                else:\n",
    "                    acc_sum += (net(x).argmax(dim=1)==y).float().sum().item()\n",
    "            n += y.shape[0]\n",
    "    return acc_sum/n\n",
    "\n",
    "def train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs):\n",
    "    net = net.to(device)\n",
    "    print('training on ', device)\n",
    "    loss = torch.nn.CrossEntropyLoss()\n",
    "    for epoch in range(num_epochs):\n",
    "        train_ls_sum, train_acc_sum = 0.0, 0.0\n",
    "        n, batch_count, start = 0, 0, time.time()\n",
    "        for X, y in train_iter:\n",
    "            X = X.to(device)\n",
    "            y = y.to(device)\n",
    "            y_hat = net(X)\n",
    "            l = loss(y_hat, y)\n",
    "            optimizer.zero_grad()\n",
    "            l.backward()\n",
    "            optimizer.step()\n",
    "            train_ls_sum += l.cpu().item()\n",
    "            train_acc_sum += (y_hat.argmax(dim=1)==y).sum().cpu().item()\n",
    "            n += y.shape[0]\n",
    "            batch_count += 1\n",
    "        test_acc = evaluate_accuracy(test_iter, net)\n",
    "        print('epoch %d, loss %.4f, train_acc %.3f, test_acc %.3f, time %.1f sec' % (epoch + 1, train_ls_sum/batch_count, train_acc_sum/n, test_acc, time.time() - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training on  cuda\n",
      "epoch 1, loss 0.3921, train_acc 0.856, test_acc 0.882, time 200.0 sec\n",
      "epoch 2, loss 0.2526, train_acc 0.907, test_acc 0.893, time 197.1 sec\n",
      "epoch 3, loss 0.2098, train_acc 0.922, test_acc 0.918, time 197.5 sec\n",
      "epoch 4, loss 0.1863, train_acc 0.932, test_acc 0.918, time 197.5 sec\n",
      "epoch 5, loss 0.1619, train_acc 0.940, test_acc 0.910, time 197.7 sec\n"
     ]
    }
   ],
   "source": [
    "batch_size = 128\n",
    "# 如出现“out of memory”的报错信息，可减小batch_size或resize\n",
    "train_iter, test_iter = load_data_fashion_mnist(batch_size, resize=96)\n",
    "lr, num_epochs = 0.001, 5\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "train_model(net, train_iter, test_iter, batch_size, optimizer, device, num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小结\n",
    "1、残差块通过跨层的数据通道从而能够训练出有效的深度神经网络。\n",
    "\n",
    "2、ResNet深刻影响了后来的深度神经网络的设计。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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